Data For Model Validation Research Articles

Investigation of key parameters for the operation of a sodium heat pipe with visualization using X-ray radiography

Alkali metal heat pipes are considered as a promising primary cooling system of special-purpose nuclear reactors. For the deployment, it is important to understand the heat pipe’s behaviors under different operation conditions. In this paper, the startup characteristics and operation performance of three sodium heat pipes with 67%, 102% and 172% sodium filling ratios, respectively, were investigated, and the two-phase sodium flow was visualized using high-speed, high-resolution x-ray radiography. The boiling flow regimes in the evaporator region were categorized into stagnant sodium liquid pool, geyser boiling and developed boiling. The effect of key parameters, such as the boundary conditions of the evaporator and condenser, the sodium filling ratio, and the inclination angle were investigated. It was found that the heater power and condenser cooling conditions have a great influence on the operation temperature within the achievable range of heat transfer rate. The filling ratio and inclination angle affect the boiling phenomena and the heat pipe operational range. The experiments can provide valuable data for heat pipe model validation as well as the design of the special-purpose nuclear reactor and other high-temperature heat pipe systems.

On the use of matrix profiles and optimal transport theory for multivariate time series anomaly detection within structural health monitoring

In order for a practical application of structural health monitoring to be considered successful, not only is the detection of anomalies important but so is the tracking of various other behaviors, which may be induced by changing operational conditions or environmental variations. Rather than being problems of anomaly detection, these are known as problems of changing contexts or changing semantics. In this paper, we propose a novel, and efficient method which is able to perform both tasks: anomaly detection, and semantic analysis, simultaneously, and which is also able to scale to a multi-sensor network array. The proposed methodology works to combine a recent development from the field of time series data mining known as matrix profiling, as well as ideas from a popular area of mathematics known as optimal transport theory. In particular, matrix profiles will be used to generate curves of semantic segmentation, over multivariate time series data. From here, it will be demonstrated that by finding the unbalanced optimal transport barycenter one can successfully pool together information from a sensor array network in such a way that the presence of false positive transition points is reduced significantly when compared to the baseline mean. Moreover, this will be demonstrated to occur in a hierarchical fashion by employing a variational mode decomposition across each sensor in the network array. Overall, the proposed methodology: (i) Can work in the small data context without requiring a large amount of data for model training and validation, (ii) Applies readily to unsupervised and semi-supervised learning settings, (iii) Can extend to the online learning setting, (iv) Is adaptable and robust for sensor array network problems, (v) Does not require specifying any threshold, and (vi) Is highly interpretable. Finally, the proposed methodology will be validated on three different levels of investigation: (i) A numerical dataset of an aerospace model based on a two-dimensional plate model, (ii) An experimental dataset of a building from Los Alamos National Laboratories, and finally, (iii) A field investigation on a cable-stayed bridge located in Sydney, Australia.

Developing an Ovine Model of Impact Traumatic Brain Injury

<div>Traumatic brain injury is a leading cause of global death and disability. Clinically relevant large animal models are a vital tool for understanding the biomechanics of injury, providing validation data for computation models, and advancing clinical translation of laboratory findings. It is well-established that large angular accelerations of the head can cause TBI, but the effect of head impact on the extent and severity of brain pathology remains unclear. Clinically, most TBIs occur with direct head impact, as opposed to inertial injuries where the head is accelerated without direct impact. There are currently no active large animal models of impact TBI. Sheep may provide a valuable model for studying TBI biomechanics, with relatively large brains that are similar in structure to that of humans. The aim of this project is to develop an ovine model of impact TBI to study the relationships between impact mechanics and brain pathology. An elastic energy impact injury device has been developed to apply scalable head impacts to rapidly rotate the head without causing hard tissue damage. A motion constraint device has been developed to limit the head motion to a single plane of rotation. The apparatus has been tested using deceased animals to assess the controllability of impact velocities, the repeatability of head kinematics, and the dynamic response of the head to impact. Impact velocities are effectively controlled by modulating the elastic energy stored in the impact piston. The resulting head kinematics are somewhat variable, and are influenced by impact location, time-dependent postmortem tissue changes, and specimen head and neck physiology. Model development will continue, and in vivo testing will be conducted to assess the brain pathology following impacts of varying severity.</div>

BEHAVIOR OF VARIABLE DENSITY MUNITIONS UNDER DAM BREAK FORCING

Munitions or Unexploded Ordnance (UXO) are ammunitions belonging to a larger family of explosives from past military activities. Sea disposal of munitions was a common practice from the late 1800s to 1970 when international conventions put an end to the practice. The exact quantity of munitions dumped into the Oceans globally is unknown due to sparse documentation but conservative estimates of known records stand at 1.6 million tons (Wilkinson, 2017). After decades underwater, some munitions have resurfaced in the nearshore, presumably washed onshore or exhumed by high-energy wave action. Extreme events could be major causes of migration and exposure of UXO in the nearshore. The quantification of variable density munitions behavior in the swash zone remains poorly understood. Biofouling, encrustation, and corrosion can alter the density of the underwater munitions, which consequently impacts the behavior of the munitions in the swash zone. Hence, this experimental study aimed to quantify the behavior of variable density munitions in the swash zone under dam-break scenarios. The findings of the study create more insights into the behavior of variable density munitions in the swash zone and can also serve as validation data for probabilistic models on munitions behavior in the swash zone under extreme events.

Vision-Assisted Millimeter-Wave Beam Management for Next-Generation Wireless Systems: Concepts, Solutions, and Open Challenges

Beamforming techniques have been widely used in the millimeter-wave (mm-wave) bands to mitigate the path loss of mm-wave radio links as narrow straight beams by directionally concentrating the signal energy. However, traditional mm-wave beam management algorithms usually require excessive channel state information (CSI) overhead, leading to extremely high computational and communication costs. This hinders the widespread deployment of mm-wave communications. By contrast, the revolutionary vision-assisted beam management system concept employed at base stations (BSs) can select the optimal beam for the target user equipment (UE), based on location information determined by machine learning (ML) algorithms applied to visual data, without requiring channel information. In this article, we present a comprehensive framework for a vision-assisted mm-wave beam management system, its typical deployment scenarios, and the specifics of the framework. Then, some of the challenges faced by this system and their efficient solutions are discussed from the perspective of ML. Next, a new simulation platform is conceived to provide both visual and wireless data for model validation and performance evaluation. Our simulation results indicate that vision-assisted beam management is indeed attractive for next-generation wireless systems.

Perceived uncertainty and switching intention on e-commerce platforms: The moderating role of usage habit

With the increasingly fierce competition between e-commerce platforms, the issue of user retention has received more attention. Prior research highlighted the important role of perceived uncertainty in predicting user switching. This study enriches this line of literature by incorporating the effect of habit. Distinguished from other research, our study specifically focuses on the conflict between habit, which requires a stable environment for formation, and perceived uncertainty, which indicates an unstable environment. Anchored on the stimulus–organism–response framework, we provide a clear explanation of how perceived uncertainty interacts with habit and shapes users’ switching intention in the e-commerce context. An online survey of 358 users of the Taobao in China provided data for model validation and partial least squares estimation was adopted for data analysis. Three main findings emerged. First, perceived information asymmetry and perceived seller opportunism increase users’ perceived uncertainty. Second, perceived uncertainty directly and positively influences disconfirmation (internal cognition), whereas indirectly influences dissatisfaction (internal affect) through disconfirmation. Third, users’ habit of using an incumbent platform negatively moderates the effect of perceived uncertainty on disconfirmation. Furthermore, both disconfirmation and dissatisfaction positively contribute to the users’ switching intention. The findings provide valuable enlightenment for e-commerce platform owners to retain users.

A pathway model of glucose-stimulated insulin secretion in the pancreatic β-cell.

The pancreas plays a critical role in maintaining glucose homeostasis through the secretion of hormones from the islets of Langerhans. Glucose-stimulated insulin secretion (GSIS) by the pancreatic β-cell is the main mechanism for reducing elevated plasma glucose. Here we present a systematic modeling workflow for the development of kinetic pathway models using the Systems Biology Markup Language (SBML). Steps include retrieval of information from databases, curation of experimental and clinical data for model calibration and validation, integration of heterogeneous data including absolute and relative measurements, unit normalization, data normalization, and model annotation. An important factor was the reproducibility and exchangeability of the model, which allowed the use of various existing tools. The workflow was applied to construct a novel data-driven kinetic model of GSIS in the pancreatic β-cell based on experimental and clinical data from 39 studies spanning 50 years of pancreatic, islet, and β-cell research in humans, rats, mice, and cell lines. The model consists of detailed glycolysis and phenomenological equations for insulin secretion coupled to cellular energy state, ATP dynamics and (ATP/ADP ratio). Key findings of our work are that in GSIS there is a glucose-dependent increase in almost all intermediates of glycolysis. This increase in glycolytic metabolites is accompanied by an increase in energy metabolites, especially ATP and NADH. One of the few decreasing metabolites is ADP, which, in combination with the increase in ATP, results in a large increase in ATP/ADP ratios in the β-cell with increasing glucose. Insulin secretion is dependent on ATP/ADP, resulting in glucose-stimulated insulin secretion. The observed glucose-dependent increase in glycolytic intermediates and the resulting change in ATP/ADP ratios and insulin secretion is a robust phenomenon observed across data sets, experimental systems and species. Model predictions of the glucose-dependent response of glycolytic intermediates and biphasic insulin secretion are in good agreement with experimental measurements. Our model predicts that factors affecting ATP consumption, ATP formation, hexokinase, phosphofructokinase, and ATP/ADP-dependent insulin secretion have a major effect on GSIS. In conclusion, we have developed and applied a systematic modeling workflow for pathway models that allowed us to gain insight into key mechanisms in GSIS in the pancreatic β-cell.

High temperature and pressure Gladstone–Dale coefficient measurements in air behind reflected shock waves

Understanding the optical properties of air is essential for the validation and characterization of plasmas and hypersonic flows. Beyond 6000 K, the dissociation of nitrogen and oxygen molecules, along with other reactions, alters the equilibrium composition of air, causing a temperature and pressure dependence in the Gladstone–Dale coefficient. Due to measurement complexities, there is currently very little experimental data to validate model predictions under these conditions. In this work, a unique quadrature fringe imaging interferometer technique is applied to high temperature and pressure measurements of air in the Sandia free-piston high enthalpy shock tube. The diagnostic method combines a narrowband and broadband source to capture large, nearly-discrete changes in the index of refraction by calibrating to interference pattern changes. For the experiments, the reflected shock front is used to generate temperatures between 6000 and 7800 K at pressures up to 300 psi (20 bars). Results behind the shock front exhibit complex flow bifurcation and tail shock feature before equilibrium conditions are reached. Measurements in these flows show close agreement with theoretical predictions of the nonconstant Gladstone–Dale coefficient at high temperatures and high pressures, providing new validation data for chemical equilibrium gas models.

Optimization of photoanode on dye-sensitized solar cell structure using k-nearest neighbor method

Dye-sensitized solar cells (DSSC) have recently gained significant attention in a number of markets and are recognized as a better option for energy generation than conventional ones, providing clean, sustainable, and renewable green energy. Several studies have focused on photoanode optimization using machine learning. Photoanode produces performance results in the form of Voc and Isc, which are then used as model training and validation data. The highest predictive results were obtained for A3+A2 (TiO2 18NR-T and TiO2 R/SP). The TiO2 layers combinations were prepared according to the optimization training performed was carried out with an efficiency of 2.630%.

Characterisation of the transient mixing behaviour of evaporating near-critical droplets

With technical progress, combustion pressures have been increased over the years, frequently exceeding the critical pressure of the injected fluids. For conditions beyond the critical point of the injected fluids, the fundamental physics of mixing and evaporation processes is not yet fully understood. In particular, quantitative data for validation of numerical simulations and analytical models remain sparse. In previous works, transient speed of sound studies applying laser-induced thermal acoustics (LITA) have been conducted to investigate the mixing behaviour in the wake of an evaporating droplet injected into a supercritical atmosphere. LITA is a seedless, non-intrusive measurement technique capable of direct speed of sound measurements within these mixing processes. The used setup employs a high-repetition-rate excitation laser source and, therefore, allows the acquisition of time-resolved speed of sound data. For the visualisation of the evaporation process, measurements are accompanied by direct, high-speed shadowgraphy. In the present work, the measured speed of sound data are evaluated by applying an advection-controlled mixing assumption to estimate both the local mole fraction and mixing temperature. For this purpose, planar spontaneous Raman scattering results measured under the same operating conditions are evaluated using an advection-controlled mixing assumption with the perturbed-chain statistical associating fluid theory (PC-SAFT) equation of state. Successively, the resulting concentration–temperature field is used for the estimation of local mixture parameters from the detected speed of sound data. Moreover, models using the PC-SAFT equation of state and the NIST database for the computation of the speed of sound are compared. The investigations indicate a classical two-phase evaporation process with evaporative cooling of the droplet. The subsequent mixing of fluid vapour and ambient gas also remains subcritical in the direct vicinity of the droplet.

High temperature oxidation of residual oil pyrolysis intermediates for modeling gasification and combustion processes

Developing chemical kinetic models for residual oil pyrolysis, gasification, and combustion is very challenging due to the large number of components and molecular complexity. This study provides experimental data for model development and validation for two residual oils, heavy fuel oil (HFO) and vacuum residual oil (VRO). This study aims to identify and quantify the HFO and VRO pyrolysis intermediates and validate an oxidation chemical kinetic model of the pyrolysis intermediates to provide data for future residual oil model development using hybrid chemistry (HyChem) and functional groups for mechanism development (FGMech) approaches. The pyrolysis experiments were conducted in a bench-scale tubular reactor at atmospheric pressure and a temperature of 1273 K. Detailed compositions of the yields from the pyrolysis of HFO and VRO were analyzed using gas chromatography-flame ionization detection (GC-FID) for the gas yields and gas chromatography-mass spectrometry/flame ionization detection (GC–MS/FID) for the liquids. Elemental analysis was conducted for the solid residuals. The experiment results show similar pyrolysis products from HFO and VRO. A surrogate of major components of the gas products and representatives of benzene and naphthalene derivatives (major compounds identified in liquid products) of the liquid products was formulated and its chemistry was studied by measuring ignition delay times (IDTs) in a shock tube. IDT measurements were conducted for stoichiometric and rich (ϕ = 1 and 3) mixtures, pressures of 20 and 40 bar, and diluents of nitrogen and carbon dioxide. Brute force IDT sensitivity analyses were used to study the effect of pressure, equivalence ratio and diluents on the reactivity of the mixture.

Research on Multi-Port Ship Traffic Prediction Method Based on Spatiotemporal Graph Neural Networks

The intelligent maritime transportation system has emerged as a pivotal component in port management, owing to the rapid advancements in artificial intelligence and big data technology. Its essence lies in the application of digital modeling techniques, which leverage extensive ship data to facilitate efficient operations. In this regard, effective modeling and accurate prediction of the fluctuation patterns of ship traffic in multiple port regions will provide data support for trade analysis, port construction planning, and traffic safety management. In order to better express the potential interdependencies between ports, inspired by graph neural networks, this paper proposes a data-driven approach to construct a multi-port network and designs a spatiotemporal graph neural network model. The model incorporates graph attention networks and a dilated causal convolutional architecture to capture the temporal and spatial dimensions of traffic variation patterns. It also employs a gated-mechanism-based spatiotemporal bi-dimensional feature fusion strategy to handle the potential unequal relationships between the two dimensions of features. Compared to existing methods for port traffic prediction, this model fully considers the network characteristics of the overall port and fills the research gap in multi-port scenarios. In the experiments, real port ship traffic datasets were constructed using data from the Automatic Identification System (AIS) and port geographical information data for model validation. The results demonstrate that the model exhibits outstanding robustness and performs well in predicting traffic in multiple sub-regional port clusters.

A Conceptual Framework for the SE of AI‐Intensive Systems (SE4AI) – Considering Data Through the Life‐Cycle

AbstractThis paper considers the challenge of the Systems Engineering of AI‐Intensive Systems (SE4AI) with a particular focus on their high reliance on data for model design verification, model training and model validation. The paper opens by describing the contextual background and reviewing contemporary AI/ML development approaches, identifying these to be largely software based “bottom‐up” driven by technological advancements, lacking a holistic top‐down “end‐to‐end” systems‐based perspective. Concepts and principles from systems thinking, modelling and simulation, and situation awareness are then briefly described in part two, as exaptive enabling building blocks, for integration into best‐practice evolutionary SE life‐cycle processes. A proposed tailored evolutionary SE life‐cycle process, with a focus on model design verification, model training and model validation data is then described in part three, through an example life‐cycle iteration, through each phase, in terms of the phase objective, the proposed refinement to the phase activities to cater for ML systems and the proposed phase outcomes. Related initiatives of direct relevance to this research are identified in part four, followed by a summary and conclusion.

A Note on Conservative Mixing: Implications for Selecting Salinity-Transport Model Constituents in the San Francisco Estuary

The deviation of specific electrical conductance (EC) from conservative mixing behavior is well-established in the scientific literature. This principle is based on the observation that, as salt concentration in a water sample increases, the mobility of individual ions in the sample decreases, and thus their ability to conduct electricity decreases. Despite this fact, some commonly used models for salinity transport in the San Francisco Estuary (estuary) utilize EC as a primary simulation constituent, treating it as a conservative quantity. Such a modeling approach has likely been followed to exploit the wide availability of EC data for model calibration and validation, and to obviate the need to translate between EC and salinity in a domain characterized by multiple source waters with varying ionic make-ups. Arguably, this approach provides a reasonable trade-off between data translation error and model simulation error. In this paper, we critically evaluate this approach, employing an extensive salinity data set that includes measurements of EC and major ion concentrations in the estuary. We demonstrate and quantify EC deviation from steady-state, conservative mixing behavior; review the conservative mixing behavior of three bulk salinity measures (practical salinity, ionic strength, and limiting equivalent conductance); and evaluate their source-dependent correlations with EC in the estuary. We find limiting equivalent conductance—a value that assumes uninhibited mobility among individual ions in a water sample—to be an attractive alternative for salinity transport in the estuary. In addition to being a conservative quantity, it is consistently correlated with EC in the estuary’s dominant source waters, and thus addresses concerns related to data-translation error. We conclude this paper discussing pros and cons of adopting various salinity-transport model constituents.

Simulator Aids Design, Analysis of Acidizing Jobs With Fluoroboric Acid

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 205893, “Novel Simulator for Design and Analysis of Matrix Acidizing Jobs With Fluoroboric Acid in Sandstone Reservoirs,” by Mohammed Qamruzzaman, SPE, Mandeep Khan, and Dhirendra C. Roy, SPE, ONGC, et al. The paper has not been peer reviewed. _ Matrix acidizing with fluoroboric acid (HBF4) has gained special attention because of its deeper penetration of in-situ generated hydrofluoric (HF) acid and stabilization of formation fines by binding them to the pore surface. While numerous mathematical models exist in the literature for design and evaluation of conventional mud acid treatments, few attempts have been made in developing a laboratory-validated model that can do so for fluoroboric acid treatments. The complete paper presents a novel mathematical model that has been developed that takes into account the chemical kinetics and equilibrium aspects of important reactions and fluid flow inside the reservoir rock. Mathematical Modeling Because the complete paper contains numerous equations, it is essential for understanding the authors’ description of their mathematical model. The authors identify three benefits of their approach from a modeling point of view. First, it reduces the total number of chemical reactions taking place, which greatly simplifies computational complexity while maintaining reasonable accuracy. Second, the inclusion of separate chemical reactions for silica gel precipitation may require fixing the average value of stoichiometric coefficients for reactions between HF/hexafluorosilicic acid (H2SiF6) and slow- and fast-reacting minerals. This becomes challenging to do beforehand, especially when different types of clays are present with relatively equal proportions in the reservoir. Third, all resulting nondimensional numbers can be estimated easily by measuring effluent acid concentration during coreflooding. The following assumptions have been incorporated into the model: - The reservoir and core are assumed to be homogeneous and isotropic. - Flow is assumed to be one-dimensional. - All fast-reacting minerals are grouped together as a single fast-reacting mineral. - All slow-reacting minerals are grouped together as single slow-reacting mineral. - The reaction of HF with fast- and slow-reacting minerals is assumed to be first-order with respect to HF. - Changes in porosity are small and diffusion/gravity/thermal effects are neglected. Laboratory Studies Laboratory studies were performed on sandstone core samples from a prominent payzone in northeastern India to generate data for model validation. Core plugs of 1.5-in. diameter were taken from the same block to prevent mineralogical heterogeneities. Samples were characterized with X-ray diffraction (XRD) analysis and porosity measurements. This payzone was selected because it consists of a diverse range of minerals. Thus, a model that can be validated with the coreflooding from this sample could, in principle, apply to any other sandstone reservoir.

Identification and validation of the model consisting of DDX49, EGFR, and T-stage as a possible risk factor for lymph node metastasis in patients with lung cancer.

The lymph node metastasis stage of lung cancer is an important decisive factor in the need for postoperative adjuvant treatment and the difference between stage IIIa and stage IIIB that is the necessary information to distinguish whether surgery can be performed or not. The specificity of the clinical diagnosis of lung cancer with lymph node metastasis cannot meet the requirements of preoperative evaluation of surgical indications and prediction of surgical removal range in lung cancer. This was an early experimental laboratory trial. The model identification data included the RNA sequence data of 10 patients from our clinical data and 188 patients with lung cancer from The Cancer Genome Atlas dataset. The model development and validation data consisted of RNA sequence data for 537 cases from the Gene Expression Omnibus dataset. We explore the predictive value of the model on two independent clinical data. A higher specificity of diagnostic model for patients with lung cancer with lymph node metastases consisted of DDX49, EGFR, and tumor stage (T-stage), which were the independent predictive factors. The area under the curve value, specificity, and sensitivity for predicting lymph node metastases were 0.835, 70.4%, and 78.9% at RNA expression level in the training group, and 0.681, 73.2%, and 75.7% at RNA expression level in the validation group as shown as in result part. To verify the predictive performance of the combined model for lymph node metastases, we downloaded the GSE30219 data set (n = 291) and the GSE31210 data set (n = 246) from the Gene Expression Omnibus (GEO) database as the training group and validation group, respectively. In addition, the model had a higher specificity for predicting lymph node metastases in independent tissue samples. Determination of DDX49, EGFR, and T-stage could form a novel prediction model to improve the diagnostic efficacy of lymph node metastasis in clinical application.

Qualification and Commissioning of Helium Flow Loop Experiment for Blanket Design Measurements

Sufficient cooling of plasma-facing materials remains an outstanding challenge in the design of fusion reactor blankets in commercial power demonstration plants. Due to its chemical inertness and low neutron interaction cross section, pressurized helium is a candidate coolant fluid for such systems; however, helium has a small thermal mass compared to liquid coolants, potentially reducing heat removal performance. To address this need, a number of heat transfer enhancements have been proposed to improve the cooling efficiency of such components, thereby decreasing pumping power needs and improving overall plant efficiency. Toward this end, a helium flow loop experiment (HFLE) has been designed and commissioned to test advanced passive heat transfer enhancements in unit-cell test sections, providing necessary data for model validation and subsequent system design. The HFLE is designed to provide flow of pressurized (up to 4 MPa) helium at flow rates up to 80 g/s, enabling heat transfer and pressure drop measurements in test pieces at Reynolds numbers in excess of 180 000. To explore the effects of novel and complex heat transfer enhancements, test sections are produced via additive manufacturing, providing geometries not typically obtainable by conventional machining. In this work, we present results from HFLE commissioning and the initial thermal-hydraulic tests of an additively manufactured rifled-rib test section. Results are compared to smooth pipe correlations, and plans are described for future HFLE measurements. These preliminary experiments indicate the utility of the HFLE for heat transfer enhancement testing and simulation validation activities.

Application of Building Information Modelling (BIM) Six Dimension (6D) in Green Buildings: A Case Study of Sunshine and Daylight Analysis

Although a considerable amount of research has been focused on how building information modeling (BIM) helps improve sustainable construction, BIM(6D) model validation and efficient data analysis methods are still lacking. This study aims to provide the more effective research method and analytical method. The Greenjiansville Sunshine analysis software Sun2020 and daylight analysis software Dali2020 were used for 6D modeling and simulation and real simulation of a building outside the daylight range and time. The daylight coefficients, outdoor visual field observation rate, and daylight quality were calculated for the rooms inside the building. The results show that single the daylight statistics and analysis color map can not accurately analyze whether the actual daylighting meets the demand, but combined with Power BI comprehensive analysis can get fast and accurate results. This paper provides important guidance for building researchers and practitioners to better align BIM development with green building development in the future.

Investigation of spherical and non-spherical binary particles flow characteristics in a discharge hopper

The particulate flow characteristics of granular particles including mung beans, wooden spheres, wooden cylinders, wooden cubic, plastic spheres, plastic cylinders, and their binary mixtures in a flat bottom hopper were studied experimentally using high-speed high-resolution camera recordings. An image processing method based on the color threshold was developed to calculate the area ratio for binary mixtures. The mass discharge rates, the residue inclination angles, and the changes in edge height and center height with time were also investigated. In addition, the effects of different initial packing patterns on the particulate flow, mixing and segregation behaviors were analyzed and compared. It is interesting to find that the shape of particles and the initial binary particle packing patterns have significant effects on the discharging characteristics. The results of this study provide helpful guidance for industrial applications and provide experiment data for computational model validation.

Predicting road flooding risk with crowdsourced reports and fine-grained traffic data

The objective of this study is to predict road flooding risks based on topographic, hydrologic, and temporal precipitation features using machine learning models. Existing road inundation studies either lack empirical data for model validations or focus mainly on road inundation exposure assessment based on flood maps. This study addresses this limitation by using crowdsourced and fine-grained traffic data as an indicator of road inundation, and topographic, hydrologic, and temporal precipitation features as predictor variables. Two tree-based machine learning models (random forest and AdaBoost) were then tested and trained for predicting road inundations in the contexts of 2017 Hurricane Harvey and 2019 Tropical Storm Imelda in Harris County, Texas. The findings from Hurricane Harvey indicate that precipitation is the most important feature for predicting road inundation susceptibility, and that topographic features are more critical than hydrologic features for predicting road inundations in both storm cases. The random forest and AdaBoost models had relatively high AUC scores (0.860 and 0.810 for Harvey respectively and 0.790 and 0.720 for Imelda respectively) with the random forest model performing better in both cases. The random forest model showed stable performance for Harvey, while varying significantly for Imelda. This study advances the emerging field of smart flood resilience in terms of predictive flood risk mapping at the road level. In particular, such models could help impacted communities and emergency management agencies develop better preparedness and response strategies with improved situational awareness of road inundation likelihood as an extreme weather event unfolds.